01
Connect
Connected fragmented clinical knowledge, coding rules, and case context in one governed environment.
A shared foundation for the information used in each recommendation.
Healthcare & Life Sciences
Client engagement
Clinical knowledge turned into ranked, reviewable recommendations.
Raava built a claims coding decision platform that brings fragmented clinical knowledge into governed rules, ranks recommendations with confidence scoring, and captures each decision for future ML training data.
Client engagement
Healthcare & Life Sciences
Complex clinical knowledge was fragmented across manual lookup processes.
Claims coding decisions depended on expertise spread across reference material, coding rules, and case context. Clinical teams carried the burden of finding and interpreting that information before they could act.
The decision path was difficult to scale or improve.
Manual lookup limited consistency and left valuable decision feedback outside the system, making it harder to create structured training data for future classification models.
Raava intervention
Rules-based decision logic brought expert knowledge into the workflow.
Raava encoded complex clinical expertise as governed decision logic, then delivered ranked recommendations with confidence scoring and a feedback loop that captures user decisions as future ML training data.
How we delivered
Built around the coding decision at the point of work.
The engagement connected the knowledge required for the decision, modeled expert rules and ranking logic, and delivered guidance with a structured feedback path.
02
Model
Encoded complex clinical expertise as rules-based decision logic with ranking and confidence scoring.
Reviewable logic behind ranked recommendations.
03
Deliver
Delivered recommendations in the workflow and captured user decisions as structured data for future ML classification.
A production feedback loop collecting decision data for future model development.
Operational change
A faster, more consistent decision path with learning built in.
The platform supports claims coding decisions today while creating the structured feedback needed to improve future classification capabilities.
- Reduced reliance on manual lookup processes for claims coding decisions.
- Ranked recommendations with confidence scoring available in production.
- A feedback-loop architecture captures decisions as future ML training data.
Turn specialized knowledge into a governed decision workflow.
Start with the high-friction decision where fragmented knowledge and manual lookup create the greatest operational burden.